ABSTRACT
Chronic heart failure, pulmonary hypertension, acute respiratory distress syndrome (ARDS), coronavirus disease (COVID), and kidney failure are leading causes of death in the U.S. and across the globe. The cornerstone for managing these diseases is assessing patients’volume fluid status in lungs. Available methods for measuring fluid accumulation in lungs are either expensive and invasive, thus unsuitable for continuous monitoring, or inaccurate and unreliable. With the recent COVID-19 epidemic, the development of a non-invasive, affordable, and accurate method for assessing lung water content in patients became utmost priority for controlling these widespread respiratory related diseases. In this paper, we propose a novel approach for non-invasive assessment of lung water content in patients. The assessment includes quantitative baseline assessment of fluid accumulation in lungs (normal, moderate edema, edema), as well as continuous monitoring of changes in lung water content. The proposed method is based on using a pair of chest patch radio frequency (RF) sensors and measuring the scattering parameters (S-parameters) of a 915-MHz signal transmitted into the body. To conduct an extensive computational study and validate our results, we utilize a National Institute of Health (NIH) database of computerized tomography (CT) scans of lungs in a diverse population of patients. An automatic workflow is proposed to convert CT scan images to three-dimensional lung objects in High-Frequency Simulation Software and obtain the S-parameters of the lungs at different water levels. Then a personalized machine learning model is developed to assess lung water status based on patient attributes and S-parameter measurements. Decision trees are chosen as our models for the superior accuracy and interpretability. Important patient attributes are identified for lung water assessment. A “cluster-then-predict”approach is adopted, where we cluster the patients based on their ages and fat thickness and train a decision tree for each cluster, resulting in simpler and more interpretable decision trees with improved accuracy. The developed machine learning models achieve areas under the receiver operating characteristic curve of 0.719 and 0.756 for 115 male and 119 female patients, respectively. These results suggest that the proposed “Chest Patch”RF sensors and machine learning models present a promising approach for non-invasive monitoring of patients with respiratory diseases. Author
ABSTRACT
During the Covid-19 global pandemic, exposure to cold cargo surfaces contaminated with Covid-19 was first identified as a potential cause of infection. Given that the epidemic situation in China is basically stable, epidemic prevention and control of cold chain cargo handling operations in Chinese ports is one of the key points for the country. It is concluded that the main risk link that may cause the spread of the epidemic is the unpacking of cold chain cargo containers by analyzing the process and characteristics of port cold chain cargo handling.In order to prevent imported epidemics from abroad, Chinese ports have taken countermeasures such as virus detection and sterilization. At present, nucleic acid detection measures have been adopted for the virus detection on the surface of goods, but the sampling quantity and method are lack of unified regulations, and the detection takes a long time. The mobile cabin PCR laboratories are used in some areas to improve the timeliness, and the virus detection on the surface of goods needs more sensitive and rapid detection technology. In the process of comprehensive preventive disinfection of goods, it was found that the disinfection efficiency of common disinfectants in low-temperature environment was greatly reduced, and a variety of new low-temperature disinfectants were rapidly developed. The disinfection technology based on deep UV LED, UV catalysis, nuclear radiation and other physical technologies have brought a new revolution to the disinfection of the new coronavirus on the surface of low-temperature objects.Due to the global pandemic of novel coronavirus and its continuous variation, technical measures for epidemic prevention and control have developed rapidly. From the prevention and control experience of Chinese ports in combating the epidemic, epidemic prevention and control is a systematic project, which requires the combination of various technical measures and close cooperation of multiple links. © 2022 SPIE.
ABSTRACT
In 2020, the COVID-19 pandemic claimed 3 million lives worldwide in span of a year;the death toll is still on rise as of writing of this article. Hospitals around the globe overwhelmed with COVID-19 patients faced medical resource shortages preventing them from providing services to even severe cases, leaving patients to selfcare. The identified COVID-19 patients had to observe the symptoms escalation or take imaging tests such as CT scans to determine the disease progression. While these imaging methods provide detailed accounts of damage inflicted to lungs by COVID-19, they have their own limitations and risks. In this article, we use computer simulations to examine the possibility of using the Cardio-Pulmonary Stethoscope (CPS) to continually monitor the COVID-19 afflicted lungs. Using a CT scan of a real COVID-19 patient, an infection was introduced in the lungs of an anatomically correct digital human model to be studied using simulation method. The preliminary results of simulations showed that the least detectable size of infection was an ellipsoid of 0.9 cubic cm, and the CPS was most sensitive while detecting infection in the lungs without preexisting conditions like edema. Based on the results and resolution, signal sensitivity of the CPS to COVID-19 infection is established and it can be argued that CPS could be an alternative method for continuous monitoring of COVID-19 disease.
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Objective To explore the impact of Coronavirus disease 2019 (COVID-19) epidemic on career choosing perspective among medical students and to analyze the related factors. Methods Semi-structured telephone interviews were conducted during March 1-25 2020 among 19 medical students of 8-year program from Peking Union Medical College. The grounded theory and thematic analysis were applied to code the data and identify categories and factors. Results Among the 19 respondents aged 19-26 years, 9 were males and 10 were female;10 were at the clinical stage, and 9 were at the premedical stage;3 respondents had family members involved in medical profession. Thematic analysis identified 6 main categories that affect the variability of medical students' career prospects under the COVID-19 pandemic. The 6 themes were individual characteristics of students;occupational characteristics;systemic factors;COVID-19 events;stressors of physicians and influence of job satisfaction. The outbreak affected everyone's mind of future career to varying degrees. The participants had been exposed to more negative aspects, while only one participant changed her career intention. There were conflicting views on whether to choose some specialties in future, such as respiratory medicine, infectious disease and critical care medicine. The participants feel more pressure as a doctor from the attitude of the public. Almost all participants mentioned feeling unsafe due to the high risk of occupational exposure and doctor-patient relationships. Most valued the support from their family, faculty, classmates, and volunteers. Many participants expressed their hope to improve the medical policies and systems. Conclusions The influence of COVID-19 outbreak on medical students' career choosing can be positive as well as negative in different degrees. However, we found no evidence that it altered their perspectives substantially. © 2022 Chinese Journal of Psychiatry. All rights reserved.
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Accurate location of pedestrians plays a crucial role in emergency relief, traffic control, crowd behavior analysis and other aspects. Especially in the context of the COVID-19 pandemic in recent years, pedestrian location technology can help relevant departments to complete target screening more quickly.However, the Pedestrian Dead Reckoning algorithm can only calculate the target trajectory through the sensor return value, but can not carry out real-time trajectory correction and location.With the rapid development of deep learning, object detection and tracking technology based on computer vision has been applied to pedestrian location, but there are two challenges in the application process.Firstly, in the pedestrian gathering scene, the target number base is large, so the accuracy of the current detection algorithm needs to be improved and the model drift index of the tracking algorithm needs to be reduced. Secondly, there is a certain distortion between the real three-dimensional coordinate space of pedestrians and the two-dimensional image captured by the camera, and the transformation of the spatial coordinate of the target point is a technical difficulty.In this regard, first of all, to improve the accuracy of pedestrian target detection in crowded scenes, this paper adopts the method of improving the generalization of network to pedestrian target, and uses k-means algorithm to find the best prior frame of pedestrian, and sets the width to height ratio suitable for the target.Secondly, to solve the problem of model drift in the above tracking process, this paper proposes a binary classification model based on target appearance difference, which introduces target context information as a new target distinguishing feature when two or more targets are similar.Finally, in order to obtain more accurate coordinate position information, this paper combines the inverse perspective algorithm to calculate the target coordinates into the coordinates in the world coordinate system, and calculates the exact position of the target in the aerial view, as well as the distance between the targets or the current flow of people.In order to evaluate the effectiveness of the proposed algorithm, experiments on target detection, tracking and precise positioning were carried out in different intensity scenarios to verify the feasibility of the proposed method.
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The teaching of electromagnetics courses faced significant challenges during the COVID-19 epidemic era. Convergence to online and virtual teaching, added significant difficulties in motivating students and ineffectively explaining and demonstrating this what is considered and highly mathematical subject. Our team at the University of Hawaii quickly resorted to the CAEME software and extensively used available multimedia models, virtual labs demos, and simulation software to help with teaching in this completely virtual teaching modality. We also developed 'Virtual Organization' website, to help students share their simulation results and discuss issues and bottle necks in learning the presented material. This paper highlights some of these ongoing activities and provide a brief assessment on the viability of the adopted approach. © 2021 IEEE.
ABSTRACT
The COVID-19 disease recognized as pandemic in 2020 created worldwide shortage of medical resources causing hospitals to admit only the severe cases and leaving the rest to selfcare. The identified covid19 patient with non-life-threatening stage had to monitor the disease progression based on the escalation of symptoms or do imaging tests like CT scans, x-ray. The imaging method while reliable comes with its own limitation and risks of exposure. In this paper, we checked the efficacy of utilizing Cardio-Pulmonary Stethoscope (CPS) to monitor the COVID-19 patient and assess the disease progressive status. The simulations were ran on anatomically realistic human model with infection at various location, size and spread reflecting real COVID-19 infected lungs. The least detectable size of injury was found to be the ellipsoid of 0.9 cubic cm, and the most consistent result was observed in the healthy lung with water content of 20%. The results presented in this paper suggest that CPS could be used as the alternative to CT scans for continuous monitoring. © 2021 IEEE.